|Description (include details on usage, files and paper references)||A large number of applications using unmanned aerial vehicle (UAV) sensors and platforms is being developed, for agriculture, logistics, recreational and military purposes. A branch of these applications uses the UAV exclusively for remote sensing purposes (RS), acquiring either top-view or oblique data that can be further processed at a centralized node.
Simultaneously, being at the core of video surveillance analysis, growing research efforts have been putted in the development of pedestrian re-identification and search methods able to work in real-world conditions, which is seen as a grand challenge. In particular, the problem of identifying pedestrians in crowded scenes based on very low resolution and partially occluded data becomes much harder in the multi-camera/multi-session mode, when matching data acquired in different places and with time lapses that deny the use of clothing information.
To date, the evaluation of pedestrian identification techniques has been conducted mostly on tracking databases (such as PETS, VIPeR, ETHZ and i-LIDS), with limited availability of soft biometric information, or even on gait recognition datasets (e.g., CASIA), which data acquisition conditions are highly dissimilar of the typical occurring in surveillance environments.
As a tool to support the research on pedestrian detection, tracking, re-identification and search methods, the P-DESTRE is a multi-session dataset of videos of pedestrians in outdoor public environments, fully annotated at the frame level for:
1) ID. Each pedestrian has a unique identifier that is kept among the data acquisition sessions, which enables to use the dataset for pedestrian re-identification;
2) Bounding box. The relative position of each pedestrian in the scene is provided as a bounding box, for every frame of the dataset, which also enables to use the data for object detection/semantic segmentation purposes;
3) Soft biometrics. Each subject of the dataset is fully characterised using 16 labels: gender, age, height, body volume, ethnicity, hair color, hairstyle, beard, mustache, glasses, head accessories, action, accessories and clothing information (x3), which enables to use the dataset also for evaluating soft biometrics inference and inference techniques.